Overview

Dataset statistics

Number of variables29
Number of observations45346
Missing cells90383
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.2 MiB
Average record size in memory1.7 KiB

Variable types

Numeric12
Text13
DateTime1
Categorical3

Alerts

status is highly imbalanced (97.0%)Imbalance
overview has 941 (2.1%) missing valuesMissing
tagline has 24959 (55.0%) missing valuesMissing
genresid has 2384 (5.3%) missing valuesMissing
genresname has 2384 (5.3%) missing valuesMissing
production_companiesname has 11789 (26.0%) missing valuesMissing
production_companiesid has 11789 (26.0%) missing valuesMissing
production_countriesiso_3166_1 has 6209 (13.7%) missing valuesMissing
production_countriesname has 6208 (13.7%) missing valuesMissing
spoken_languagesiso_639_1 has 3766 (8.3%) missing valuesMissing
spoken_languagesname has 3987 (8.8%) missing valuesMissing
spoken_languages_names has 3987 (8.8%) missing valuesMissing
spoken_languages_iso has 3766 (8.3%) missing valuesMissing
genres_ids has 2384 (5.3%) missing valuesMissing
genres_names has 2384 (5.3%) missing valuesMissing
actores has 2349 (5.2%) missing valuesMissing
director has 760 (1.7%) missing valuesMissing
popularity is highly skewed (γ1 = 29.21542294)Skewed
return is highly skewed (γ1 = 138.283787)Skewed
id has unique valuesUnique
budget has 36470 (80.4%) zerosZeros
revenue has 37949 (83.7%) zerosZeros
runtime has 1535 (3.4%) zerosZeros
vote_average has 2944 (6.5%) zerosZeros
vote_count has 2846 (6.3%) zerosZeros
return has 39971 (88.1%) zerosZeros

Reproduction

Analysis started2024-07-07 19:42:40.413933
Analysis finished2024-07-07 19:43:06.587939
Duration26.17 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

budget
Real number (ℝ)

ZEROS 

Distinct1223
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4232579.8
Minimum0
Maximum3.8 × 108
Zeros36470
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:06.672575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25000000
Maximum3.8 × 108
Range3.8 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17443731
Coefficient of variation (CV)4.1213
Kurtosis66.618217
Mean4232579.8
Median Absolute Deviation (MAD)0
Skewness7.1180066
Sum1.9193056 × 1011
Variance3.0428374 × 1014
MonotonicityNot monotonic
2024-07-07T16:43:06.815576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36470
80.4%
5000000 286
 
0.6%
10000000 258
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 189
 
0.4%
Other values (1213) 6806
 
15.0%
ValueCountFrequency (%)
0 36470
80.4%
1 25
 
0.1%
2 14
 
< 0.1%
3 9
 
< 0.1%
4 7
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
380000000 1
 
< 0.1%
300000000 1
 
< 0.1%
280000000 1
 
< 0.1%
270000000 1
 
< 0.1%
260000000 3
 
< 0.1%
258000000 1
 
< 0.1%
255000000 1
 
< 0.1%
250000000 10
< 0.1%
245000000 2
 
< 0.1%
237000000 1
 
< 0.1%

id
Real number (ℝ)

UNIQUE 

Distinct45346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108042.22
Minimum2
Maximum469172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:06.964266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5340.25
Q126390.25
median59852.5
Q3156601.5
95-th percentile357370.75
Maximum469172
Range469170
Interquartile range (IQR)130211.25

Descriptive statistics

Standard deviation112187.33
Coefficient of variation (CV)1.0383656
Kurtosis0.55836782
Mean108042.22
Median Absolute Deviation (MAD)44405
Skewness1.2828454
Sum4.8992825 × 109
Variance1.2585996 × 1010
MonotonicityNot monotonic
2024-07-07T16:43:07.102800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
862 1
 
< 0.1%
202198 1
 
< 0.1%
124026 1
 
< 0.1%
300168 1
 
< 0.1%
132316 1
 
< 0.1%
74458 1
 
< 0.1%
40777 1
 
< 0.1%
188222 1
 
< 0.1%
328483 1
 
< 0.1%
107637 1
 
< 0.1%
Other values (45336) 45336
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
469172 1
< 0.1%
468707 1
< 0.1%
468343 1
< 0.1%
467731 1
< 0.1%
465044 1
< 0.1%
464819 1
< 0.1%
464207 1
< 0.1%
464111 1
< 0.1%
463906 1
< 0.1%
463800 1
< 0.1%
Distinct89
Distinct (%)0.2%
Missing11
Missing (%)< 0.1%
Memory size2.2 MiB
2024-07-07T16:43:07.246277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters90670
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 32184
71.0%
fr 2435
 
5.4%
it 1528
 
3.4%
ja 1346
 
3.0%
de 1077
 
2.4%
es 992
 
2.2%
ru 822
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 408
 
0.9%
Other values (79) 3591
 
7.9%
2024-07-07T16:43:07.495993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34508
38.1%
n 32892
36.3%
r 3628
 
4.0%
f 2830
 
3.1%
i 2386
 
2.6%
t 2249
 
2.5%
a 1834
 
2.0%
s 1651
 
1.8%
j 1347
 
1.5%
d 1321
 
1.5%
Other values (16) 6024
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 34508
38.1%
n 32892
36.3%
r 3628
 
4.0%
f 2830
 
3.1%
i 2386
 
2.6%
t 2249
 
2.5%
a 1834
 
2.0%
s 1651
 
1.8%
j 1347
 
1.5%
d 1321
 
1.5%
Other values (16) 6024
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 34508
38.1%
n 32892
36.3%
r 3628
 
4.0%
f 2830
 
3.1%
i 2386
 
2.6%
t 2249
 
2.5%
a 1834
 
2.0%
s 1651
 
1.8%
j 1347
 
1.5%
d 1321
 
1.5%
Other values (16) 6024
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 34508
38.1%
n 32892
36.3%
r 3628
 
4.0%
f 2830
 
3.1%
i 2386
 
2.6%
t 2249
 
2.5%
a 1834
 
2.0%
s 1651
 
1.8%
j 1347
 
1.5%
d 1321
 
1.5%
Other values (16) 6024
 
6.6%

overview
Text

MISSING 

Distinct44232
Distinct (%)99.6%
Missing941
Missing (%)2.1%
Memory size19.6 MiB
2024-07-07T16:43:07.857104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1000
Median length786
Mean length323.26076
Min length1

Characters and Unicode

Total characters14354394
Distinct characters429
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44201 ?
Unique (%)99.5%

Sample

1st rowLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.
2nd rowWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.
3rd rowA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.
4th rowCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.
5th rowJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.
ValueCountFrequency (%)
the 137966
 
5.6%
a 98825
 
4.0%
and 75193
 
3.1%
to 73261
 
3.0%
of 69523
 
2.8%
in 48107
 
2.0%
is 36479
 
1.5%
his 36130
 
1.5%
with 23880
 
1.0%
her 21460
 
0.9%
Other values (97091) 1825958
74.6%
2024-07-07T16:43:08.396485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2404454
16.8%
e 1362739
 
9.5%
a 939721
 
6.5%
t 934056
 
6.5%
i 850842
 
5.9%
o 829251
 
5.8%
n 821950
 
5.7%
s 767224
 
5.3%
r 743645
 
5.2%
h 600339
 
4.2%
Other values (419) 4100173
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14354394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2404454
16.8%
e 1362739
 
9.5%
a 939721
 
6.5%
t 934056
 
6.5%
i 850842
 
5.9%
o 829251
 
5.8%
n 821950
 
5.7%
s 767224
 
5.3%
r 743645
 
5.2%
h 600339
 
4.2%
Other values (419) 4100173
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14354394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2404454
16.8%
e 1362739
 
9.5%
a 939721
 
6.5%
t 934056
 
6.5%
i 850842
 
5.9%
o 829251
 
5.8%
n 821950
 
5.7%
s 767224
 
5.3%
r 743645
 
5.2%
h 600339
 
4.2%
Other values (419) 4100173
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14354394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2404454
16.8%
e 1362739
 
9.5%
a 939721
 
6.5%
t 934056
 
6.5%
i 850842
 
5.9%
o 829251
 
5.8%
n 821950
 
5.7%
s 767224
 
5.3%
r 743645
 
5.2%
h 600339
 
4.2%
Other values (419) 4100173
28.6%

popularity
Real number (ℝ)

SKEWED 

Distinct43719
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.926188
Minimum0
Maximum547.4883
Zeros40
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:08.547481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.020823
Q10.38873225
median1.130176
Q33.6893365
95-th percentile11.063757
Maximum547.4883
Range547.4883
Interquartile range (IQR)3.3006043

Descriptive statistics

Standard deviation6.0109699
Coefficient of variation (CV)2.0541981
Kurtosis1923.3033
Mean2.926188
Median Absolute Deviation (MAD)0.967289
Skewness29.215423
Sum132690.92
Variance36.131759
MonotonicityNot monotonic
2024-07-07T16:43:08.677453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 × 10-656
 
0.1%
0.000308 42
 
0.1%
0 40
 
0.1%
0.00022 39
 
0.1%
0.001177 38
 
0.1%
0.000844 38
 
0.1%
0.000578 38
 
0.1%
0.002001 27
 
0.1%
0.003013 21
 
< 0.1%
0.00353 19
 
< 0.1%
Other values (43709) 44988
99.2%
ValueCountFrequency (%)
0 40
0.1%
1 × 10-656
0.1%
2 × 10-66
 
< 0.1%
3 × 10-66
 
< 0.1%
4 × 10-65
 
< 0.1%
5 × 10-61
 
< 0.1%
6 × 10-62
 
< 0.1%
7 × 10-61
 
< 0.1%
8 × 10-66
 
< 0.1%
9 × 10-62
 
< 0.1%
ValueCountFrequency (%)
547.488298 1
< 0.1%
294.337037 1
< 0.1%
287.253654 1
< 0.1%
228.032744 1
< 0.1%
213.849907 1
< 0.1%
187.860492 1
< 0.1%
185.330992 1
< 0.1%
185.070892 1
< 0.1%
183.870374 1
< 0.1%
154.801009 1
< 0.1%
Distinct17333
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size354.4 KiB
Minimum1874-12-09 00:00:00
Maximum2020-12-16 00:00:00
2024-07-07T16:43:08.811452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:08.953482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

revenue
Real number (ℝ)

ZEROS 

Distinct6863
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11233655
Minimum0
Maximum2.7879651 × 109
Zeros37949
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:09.085481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile48025328
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64409896
Coefficient of variation (CV)5.7336544
Kurtosis236.93621
Mean11233655
Median Absolute Deviation (MAD)0
Skewness12.251264
Sum5.0940133 × 1011
Variance4.1486347 × 1015
MonotonicityNot monotonic
2024-07-07T16:43:09.217002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37949
83.7%
12000000 20
 
< 0.1%
11000000 19
 
< 0.1%
10000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
8000000 13
 
< 0.1%
500000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7252
 
16.0%
ValueCountFrequency (%)
0 37949
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

ZEROS 

Distinct353
Distinct (%)0.8%
Missing246
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean94.177805
Minimum0
Maximum1256
Zeros1535
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:09.358866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.348775
Coefficient of variation (CV)0.40719547
Kurtosis93.913375
Mean94.177805
Median Absolute Deviation (MAD)11
Skewness4.4911098
Sum4247419
Variance1470.6286
MonotonicityNot monotonic
2024-07-07T16:43:09.702965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2548
 
5.6%
0 1535
 
3.4%
100 1470
 
3.2%
95 1409
 
3.1%
93 1212
 
2.7%
96 1104
 
2.4%
92 1078
 
2.4%
94 1061
 
2.3%
91 1055
 
2.3%
88 1030
 
2.3%
Other values (343) 31598
69.7%
ValueCountFrequency (%)
0 1535
3.4%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 50
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%

status
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing80
Missing (%)0.2%
Memory size2.5 MiB
Released
44907 
Rumored
 
229
Post Production
 
97
In Production
 
19
Planned
 
13

Length

Max length15
Median length8
Mean length8.0117528
Min length7

Characters and Unicode

Total characters362660
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 44907
99.0%
Rumored 229
 
0.5%
Post Production 97
 
0.2%
In Production 19
 
< 0.1%
Planned 13
 
< 0.1%
Canceled 1
 
< 0.1%
(Missing) 80
 
0.2%

Length

2024-07-07T16:43:09.841954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T16:43:09.958984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
released 44907
99.0%
rumored 229
 
0.5%
production 116
 
0.3%
post 97
 
0.2%
in 19
 
< 0.1%
planned 13
 
< 0.1%
canceled 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 134965
37.2%
d 45266
 
12.5%
R 45136
 
12.4%
s 45004
 
12.4%
l 44921
 
12.4%
a 44921
 
12.4%
o 558
 
0.2%
r 345
 
0.1%
u 345
 
0.1%
m 229
 
0.1%
Other values (8) 970
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 362660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 134965
37.2%
d 45266
 
12.5%
R 45136
 
12.4%
s 45004
 
12.4%
l 44921
 
12.4%
a 44921
 
12.4%
o 558
 
0.2%
r 345
 
0.1%
u 345
 
0.1%
m 229
 
0.1%
Other values (8) 970
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 362660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 134965
37.2%
d 45266
 
12.5%
R 45136
 
12.4%
s 45004
 
12.4%
l 44921
 
12.4%
a 44921
 
12.4%
o 558
 
0.2%
r 345
 
0.1%
u 345
 
0.1%
m 229
 
0.1%
Other values (8) 970
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 362660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 134965
37.2%
d 45266
 
12.5%
R 45136
 
12.4%
s 45004
 
12.4%
l 44921
 
12.4%
a 44921
 
12.4%
o 558
 
0.2%
r 345
 
0.1%
u 345
 
0.1%
m 229
 
0.1%
Other values (8) 970
 
0.3%

tagline
Text

MISSING 

Distinct20269
Distinct (%)99.4%
Missing24959
Missing (%)55.0%
Memory size2.7 MiB
2024-07-07T16:43:10.257965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length297
Median length204
Mean length46.996517
Min length1

Characters and Unicode

Total characters958118
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20173 ?
Unique (%)99.0%

Sample

1st rowRoll the dice and unleash the excitement!
2nd rowStill Yelling. Still Fighting. Still Ready for Love.
3rd rowFriends are the people who let you be yourself... and never let you forget it.
4th rowJust When His World Is Back To Normal... He's In For The Surprise Of His Life!
5th rowA Los Angeles Crime Saga
ValueCountFrequency (%)
the 10987
 
6.3%
a 6810
 
3.9%
of 4401
 
2.5%
to 3581
 
2.1%
is 2793
 
1.6%
in 2691
 
1.5%
and 2681
 
1.5%
you 2388
 
1.4%
1580
 
0.9%
for 1523
 
0.9%
Other values (15100) 134394
77.3%
2024-07-07T16:43:10.737963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153590
16.0%
e 94342
 
9.8%
t 57223
 
6.0%
o 56534
 
5.9%
a 51450
 
5.4%
n 47460
 
5.0%
i 46013
 
4.8%
r 44957
 
4.7%
s 42345
 
4.4%
h 37144
 
3.9%
Other values (160) 327060
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 958118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
153590
16.0%
e 94342
 
9.8%
t 57223
 
6.0%
o 56534
 
5.9%
a 51450
 
5.4%
n 47460
 
5.0%
i 46013
 
4.8%
r 44957
 
4.7%
s 42345
 
4.4%
h 37144
 
3.9%
Other values (160) 327060
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 958118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
153590
16.0%
e 94342
 
9.8%
t 57223
 
6.0%
o 56534
 
5.9%
a 51450
 
5.4%
n 47460
 
5.0%
i 46013
 
4.8%
r 44957
 
4.7%
s 42345
 
4.4%
h 37144
 
3.9%
Other values (160) 327060
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 958118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
153590
16.0%
e 94342
 
9.8%
t 57223
 
6.0%
o 56534
 
5.9%
a 51450
 
5.4%
n 47460
 
5.0%
i 46013
 
4.8%
r 44957
 
4.7%
s 42345
 
4.4%
h 37144
 
3.9%
Other values (160) 327060
34.1%

title
Text

Distinct42196
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-07-07T16:43:11.056966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length105
Median length79
Mean length16.702289
Min length1

Characters and Unicode

Total characters757382
Distinct characters287
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39892 ?
Unique (%)88.0%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 14544
 
10.7%
of 4923
 
3.6%
a 2238
 
1.6%
in 1693
 
1.2%
and 1629
 
1.2%
to 1053
 
0.8%
756
 
0.6%
man 665
 
0.5%
love 664
 
0.5%
for 601
 
0.4%
Other values (24353) 107329
78.9%
2024-07-07T16:43:11.542634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90771
 
12.0%
e 76195
 
10.1%
a 48911
 
6.5%
o 45636
 
6.0%
n 40797
 
5.4%
r 39993
 
5.3%
i 39748
 
5.2%
t 36706
 
4.8%
s 29500
 
3.9%
h 28499
 
3.8%
Other values (277) 280626
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 757382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
90771
 
12.0%
e 76195
 
10.1%
a 48911
 
6.5%
o 45636
 
6.0%
n 40797
 
5.4%
r 39993
 
5.3%
i 39748
 
5.2%
t 36706
 
4.8%
s 29500
 
3.9%
h 28499
 
3.8%
Other values (277) 280626
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 757382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
90771
 
12.0%
e 76195
 
10.1%
a 48911
 
6.5%
o 45636
 
6.0%
n 40797
 
5.4%
r 39993
 
5.3%
i 39748
 
5.2%
t 36706
 
4.8%
s 29500
 
3.9%
h 28499
 
3.8%
Other values (277) 280626
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 757382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
90771
 
12.0%
e 76195
 
10.1%
a 48911
 
6.5%
o 45636
 
6.0%
n 40797
 
5.4%
r 39993
 
5.3%
i 39748
 
5.2%
t 36706
 
4.8%
s 29500
 
3.9%
h 28499
 
3.8%
Other values (277) 280626
37.1%

vote_average
Real number (ℝ)

ZEROS 

Distinct92
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6241962
Minimum0
Maximum10
Zeros2944
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:11.697884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.915339
Coefficient of variation (CV)0.34055337
Kurtosis2.5420383
Mean5.6241962
Median Absolute Deviation (MAD)0.9
Skewness-1.5243174
Sum255034.8
Variance3.6685234
MonotonicityNot monotonic
2024-07-07T16:43:11.835855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2944
 
6.5%
6 2461
 
5.4%
5 1994
 
4.4%
7 1882
 
4.2%
6.5 1722
 
3.8%
6.3 1602
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1348
 
3.0%
6.7 1339
 
3.0%
Other values (82) 27304
60.2%
ValueCountFrequency (%)
0 2944
6.5%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 103
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 185
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 2
 
< 0.1%
9 158
0.3%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

ZEROS 

Distinct1820
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.13529
Minimum0
Maximum14075
Zeros2846
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:11.963495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434.75
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.89928
Coefficient of variation (CV)4.4663183
Kurtosis150.83135
Mean110.13529
Median Absolute Deviation (MAD)8
Skewness10.437494
Sum4994195
Variance241964.9
MonotonicityNot monotonic
2024-07-07T16:43:12.106465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3240
 
7.1%
2 3127
 
6.9%
0 2846
 
6.3%
3 2780
 
6.1%
4 2477
 
5.5%
5 2096
 
4.6%
6 1747
 
3.9%
7 1568
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22912
50.5%
ValueCountFrequency (%)
0 2846
6.3%
1 3240
7.1%
2 3127
6.9%
3 2780
6.1%
4 2477
5.5%
5 2096
4.6%
6 1747
3.9%
7 1568
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

genresid
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)< 0.1%
Missing2384
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean937.60851
Minimum12
Maximum10770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:12.258978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile14
Q118
median28
Q353
95-th percentile10749
Maximum10770
Range10758
Interquartile range (IQR)35

Descriptive statistics

Standard deviation2925.4542
Coefficient of variation (CV)3.1201233
Kurtosis6.936056
Mean937.60851
Median Absolute Deviation (MAD)10
Skewness2.9839567
Sum40281537
Variance8558282.4
MonotonicityNot monotonic
2024-07-07T16:43:12.376037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
18 11948
26.3%
35 8815
19.4%
28 4484
 
9.9%
99 3402
 
7.5%
27 2619
 
5.8%
80 1683
 
3.7%
53 1663
 
3.7%
12 1508
 
3.3%
10749 1191
 
2.6%
16 1122
 
2.5%
Other values (10) 4527
 
10.0%
(Missing) 2384
 
5.3%
ValueCountFrequency (%)
12 1508
 
3.3%
14 703
 
1.6%
16 1122
 
2.5%
18 11948
26.3%
27 2619
 
5.8%
28 4484
 
9.9%
35 8815
19.4%
36 278
 
0.6%
37 451
 
1.0%
53 1663
 
3.7%
ValueCountFrequency (%)
10770 389
 
0.9%
10769 118
 
0.3%
10752 379
 
0.8%
10751 524
 
1.2%
10749 1191
 
2.6%
10402 487
 
1.1%
9648 552
 
1.2%
878 646
 
1.4%
99 3402
7.5%
80 1683
3.7%

genresname
Categorical

MISSING 

Distinct20
Distinct (%)< 0.1%
Missing2384
Missing (%)5.3%
Memory size2.4 MiB
Drama
11948 
Comedy
8815 
Action
4484 
Documentary
3402 
Horror
2619 
Other values (15)
11694 

Length

Max length15
Median length11
Mean length6.532005
Min length3

Characters and Unicode

Total characters280628
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimation
2nd rowAdventure
3rd rowRomance
4th rowComedy
5th rowComedy

Common Values

ValueCountFrequency (%)
Drama 11948
26.3%
Comedy 8815
19.4%
Action 4484
 
9.9%
Documentary 3402
 
7.5%
Horror 2619
 
5.8%
Crime 1683
 
3.7%
Thriller 1663
 
3.7%
Adventure 1508
 
3.3%
Romance 1191
 
2.6%
Animation 1122
 
2.5%
Other values (10) 4527
 
10.0%
(Missing) 2384
 
5.3%

Length

2024-07-07T16:43:12.495038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama 11948
27.2%
comedy 8815
20.0%
action 4484
 
10.2%
documentary 3402
 
7.7%
horror 2619
 
6.0%
crime 1683
 
3.8%
thriller 1663
 
3.8%
adventure 1508
 
3.4%
romance 1191
 
2.7%
animation 1122
 
2.6%
Other values (12) 5562
12.6%

Most occurring characters

ValueCountFrequency (%)
a 31920
11.4%
r 31502
11.2%
m 28685
10.2%
o 25683
 
9.2%
e 23023
 
8.2%
n 15393
 
5.5%
D 15350
 
5.5%
y 14826
 
5.3%
i 13808
 
4.9%
t 13146
 
4.7%
Other values (19) 67292
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 280628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 31920
11.4%
r 31502
11.2%
m 28685
10.2%
o 25683
 
9.2%
e 23023
 
8.2%
n 15393
 
5.5%
D 15350
 
5.5%
y 14826
 
5.3%
i 13808
 
4.9%
t 13146
 
4.7%
Other values (19) 67292
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 280628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 31920
11.4%
r 31502
11.2%
m 28685
10.2%
o 25683
 
9.2%
e 23023
 
8.2%
n 15393
 
5.5%
D 15350
 
5.5%
y 14826
 
5.3%
i 13808
 
4.9%
t 13146
 
4.7%
Other values (19) 67292
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 280628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 31920
11.4%
r 31502
11.2%
m 28685
10.2%
o 25683
 
9.2%
e 23023
 
8.2%
n 15393
 
5.5%
D 15350
 
5.5%
y 14826
 
5.3%
i 13808
 
4.9%
t 13146
 
4.7%
Other values (19) 67292
24.0%
Distinct10611
Distinct (%)31.6%
Missing11789
Missing (%)26.0%
Memory size2.6 MiB
2024-07-07T16:43:12.772549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length91
Median length60
Mean length18.720863
Min length2

Characters and Unicode

Total characters628216
Distinct characters253
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7069 ?
Unique (%)21.1%

Sample

1st rowPixar Animation Studios
2nd rowTriStar Pictures
3rd rowWarner Bros.
4th rowTwentieth Century Fox Film Corporation
5th rowSandollar Productions
ValueCountFrequency (%)
pictures 6295
 
7.5%
films 4187
 
5.0%
productions 3773
 
4.5%
film 3556
 
4.3%
entertainment 2104
 
2.5%
corporation 1686
 
2.0%
fox 1039
 
1.2%
paramount 1035
 
1.2%
universal 964
 
1.2%
company 916
 
1.1%
Other values (9738) 58042
69.4%
2024-07-07T16:43:13.223499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 51265
 
8.2%
50044
 
8.0%
e 44162
 
7.0%
r 42079
 
6.7%
o 41523
 
6.6%
n 41253
 
6.6%
t 40773
 
6.5%
a 36515
 
5.8%
s 30682
 
4.9%
l 24074
 
3.8%
Other values (243) 225846
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 628216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 51265
 
8.2%
50044
 
8.0%
e 44162
 
7.0%
r 42079
 
6.7%
o 41523
 
6.6%
n 41253
 
6.6%
t 40773
 
6.5%
a 36515
 
5.8%
s 30682
 
4.9%
l 24074
 
3.8%
Other values (243) 225846
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 628216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 51265
 
8.2%
50044
 
8.0%
e 44162
 
7.0%
r 42079
 
6.7%
o 41523
 
6.6%
n 41253
 
6.6%
t 40773
 
6.5%
a 36515
 
5.8%
s 30682
 
4.9%
l 24074
 
3.8%
Other values (243) 225846
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 628216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 51265
 
8.2%
50044
 
8.0%
e 44162
 
7.0%
r 42079
 
6.7%
o 41523
 
6.6%
n 41253
 
6.6%
t 40773
 
6.5%
a 36515
 
5.8%
s 30682
 
4.9%
l 24074
 
3.8%
Other values (243) 225846
36.0%

production_companiesid
Real number (ℝ)

MISSING 

Distinct10652
Distinct (%)31.7%
Missing11789
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean10651.203
Minimum1
Maximum96067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:13.392004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q1516
median3965
Q310330
95-th percentile57185
Maximum96067
Range96066
Interquartile range (IQR)9814

Descriptive statistics

Standard deviation18150.018
Coefficient of variation (CV)1.7040345
Kurtosis6.7993213
Mean10651.203
Median Absolute Deviation (MAD)3707
Skewness2.6596116
Sum3.5742243 × 108
Variance3.2942316 × 108
MonotonicityNot monotonic
2024-07-07T16:43:13.544004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 996
 
2.2%
8411 851
 
1.9%
306 780
 
1.7%
6194 757
 
1.7%
33 754
 
1.7%
5 429
 
0.9%
441 401
 
0.9%
6 290
 
0.6%
60 272
 
0.6%
2 262
 
0.6%
Other values (10642) 27765
61.2%
(Missing) 11789
26.0%
ValueCountFrequency (%)
1 29
 
0.1%
2 262
 
0.6%
3 29
 
0.1%
4 996
2.2%
5 429
0.9%
6 290
 
0.6%
7 8
 
< 0.1%
8 50
 
0.1%
9 122
 
0.3%
10 1
 
< 0.1%
ValueCountFrequency (%)
96067 1
< 0.1%
96053 1
< 0.1%
95940 1
< 0.1%
95918 1
< 0.1%
95799 1
< 0.1%
95698 1
< 0.1%
95677 1
< 0.1%
95666 1
< 0.1%
95410 1
< 0.1%
95393 1
< 0.1%
Distinct142
Distinct (%)0.4%
Missing6209
Missing (%)13.7%
Memory size2.1 MiB
2024-07-07T16:43:13.751696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters78274
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 18410
47.0%
gb 3066
 
7.8%
fr 2699
 
6.9%
ca 1498
 
3.8%
jp 1490
 
3.8%
it 1470
 
3.8%
de 1417
 
3.6%
ru 799
 
2.0%
in 782
 
2.0%
es 601
 
1.5%
Other values (132) 6905
 
17.6%
2024-07-07T16:43:14.066287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 19947
25.5%
S 19593
25.0%
R 4981
 
6.4%
B 3724
 
4.8%
G 3310
 
4.2%
F 3033
 
3.9%
E 3026
 
3.9%
I 2985
 
3.8%
A 2546
 
3.3%
C 2169
 
2.8%
Other values (16) 12960
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 19947
25.5%
S 19593
25.0%
R 4981
 
6.4%
B 3724
 
4.8%
G 3310
 
4.2%
F 3033
 
3.9%
E 3026
 
3.9%
I 2985
 
3.8%
A 2546
 
3.3%
C 2169
 
2.8%
Other values (16) 12960
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 19947
25.5%
S 19593
25.0%
R 4981
 
6.4%
B 3724
 
4.8%
G 3310
 
4.2%
F 3033
 
3.9%
E 3026
 
3.9%
I 2985
 
3.8%
A 2546
 
3.3%
C 2169
 
2.8%
Other values (16) 12960
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 19947
25.5%
S 19593
25.0%
R 4981
 
6.4%
B 3724
 
4.8%
G 3310
 
4.2%
F 3033
 
3.9%
E 3026
 
3.9%
I 2985
 
3.8%
A 2546
 
3.3%
C 2169
 
2.8%
Other values (16) 12960
16.6%
Distinct143
Distinct (%)0.4%
Missing6208
Missing (%)13.7%
Memory size2.6 MiB
2024-07-07T16:43:14.328460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length36
Median length32
Mean length15.358169
Min length4

Characters and Unicode

Total characters601088
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.1%

Sample

1st rowUnited States of America
2nd rowUnited States of America
3rd rowUnited States of America
4th rowUnited States of America
5th rowUnited States of America
ValueCountFrequency (%)
united 21490
21.7%
states 18411
18.6%
of 18410
18.6%
america 18410
18.6%
kingdom 3066
 
3.1%
france 2699
 
2.7%
canada 1498
 
1.5%
japan 1490
 
1.5%
italy 1470
 
1.5%
germany 1422
 
1.4%
Other values (160) 10471
10.6%
2024-07-07T16:43:14.754399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 67165
11.2%
t 61834
 
10.3%
59699
 
9.9%
a 58137
 
9.7%
i 48975
 
8.1%
n 37903
 
6.3%
d 28572
 
4.8%
r 26146
 
4.3%
o 24628
 
4.1%
m 23690
 
3.9%
Other values (42) 164339
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 601088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 67165
11.2%
t 61834
 
10.3%
59699
 
9.9%
a 58137
 
9.7%
i 48975
 
8.1%
n 37903
 
6.3%
d 28572
 
4.8%
r 26146
 
4.3%
o 24628
 
4.1%
m 23690
 
3.9%
Other values (42) 164339
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 601088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 67165
11.2%
t 61834
 
10.3%
59699
 
9.9%
a 58137
 
9.7%
i 48975
 
8.1%
n 37903
 
6.3%
d 28572
 
4.8%
r 26146
 
4.3%
o 24628
 
4.1%
m 23690
 
3.9%
Other values (42) 164339
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 601088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 67165
11.2%
t 61834
 
10.3%
59699
 
9.9%
a 58137
 
9.7%
i 48975
 
8.1%
n 37903
 
6.3%
d 28572
 
4.8%
r 26146
 
4.3%
o 24628
 
4.1%
m 23690
 
3.9%
Other values (42) 164339
27.3%
Distinct116
Distinct (%)0.3%
Missing3766
Missing (%)8.3%
Memory size2.1 MiB
2024-07-07T16:43:14.944774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters83160
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 26808
64.5%
fr 2424
 
5.8%
it 1410
 
3.4%
ja 1386
 
3.3%
de 1299
 
3.1%
es 1143
 
2.7%
ru 905
 
2.2%
hi 546
 
1.3%
ko 446
 
1.1%
zh 413
 
1.0%
Other values (106) 4800
 
11.5%
2024-07-07T16:43:15.233149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 29571
35.6%
n 27668
33.3%
r 3950
 
4.7%
f 2872
 
3.5%
i 2369
 
2.8%
a 2227
 
2.7%
t 2178
 
2.6%
s 1914
 
2.3%
d 1607
 
1.9%
j 1388
 
1.7%
Other values (16) 7416
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29571
35.6%
n 27668
33.3%
r 3950
 
4.7%
f 2872
 
3.5%
i 2369
 
2.8%
a 2227
 
2.7%
t 2178
 
2.6%
s 1914
 
2.3%
d 1607
 
1.9%
j 1388
 
1.7%
Other values (16) 7416
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29571
35.6%
n 27668
33.3%
r 3950
 
4.7%
f 2872
 
3.5%
i 2369
 
2.8%
a 2227
 
2.7%
t 2178
 
2.6%
s 1914
 
2.3%
d 1607
 
1.9%
j 1388
 
1.7%
Other values (16) 7416
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29571
35.6%
n 27668
33.3%
r 3950
 
4.7%
f 2872
 
3.5%
i 2369
 
2.8%
a 2227
 
2.7%
t 2178
 
2.6%
s 1914
 
2.3%
d 1607
 
1.9%
j 1388
 
1.7%
Other values (16) 7416
 
8.9%

spoken_languagesname
Text

MISSING 

Distinct72
Distinct (%)0.2%
Missing3987
Missing (%)8.8%
Memory size2.6 MiB
2024-07-07T16:43:15.420191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length7
Mean length6.9372325
Min length3

Characters and Unicode

Total characters286917
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish
ValueCountFrequency (%)
english 26808
63.0%
français 2424
 
5.7%
italiano 1410
 
3.3%
日本語 1386
 
3.3%
deutsch 1299
 
3.1%
español 1143
 
2.7%
pусский 905
 
2.1%
हिन्दी 546
 
1.3%
한국어/조선말 446
 
1.0%
普通话 413
 
1.0%
Other values (67) 5768
 
13.6%
2024-07-07T16:43:15.737328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 34539
12.0%
n 32163
11.2%
i 31608
11.0%
l 30012
10.5%
h 28164
9.8%
E 27994
9.8%
g 27916
9.7%
a 11070
 
3.9%
o 4017
 
1.4%
r 3564
 
1.2%
Other values (160) 55870
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 34539
12.0%
n 32163
11.2%
i 31608
11.0%
l 30012
10.5%
h 28164
9.8%
E 27994
9.8%
g 27916
9.7%
a 11070
 
3.9%
o 4017
 
1.4%
r 3564
 
1.2%
Other values (160) 55870
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 34539
12.0%
n 32163
11.2%
i 31608
11.0%
l 30012
10.5%
h 28164
9.8%
E 27994
9.8%
g 27916
9.7%
a 11070
 
3.9%
o 4017
 
1.4%
r 3564
 
1.2%
Other values (160) 55870
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 34539
12.0%
n 32163
11.2%
i 31608
11.0%
l 30012
10.5%
h 28164
9.8%
E 27994
9.8%
g 27916
9.7%
a 11070
 
3.9%
o 4017
 
1.4%
r 3564
 
1.2%
Other values (160) 55870
19.5%
Distinct72
Distinct (%)0.2%
Missing3987
Missing (%)8.8%
Memory size2.6 MiB
2024-07-07T16:43:15.927397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length7
Mean length6.9372325
Min length3

Characters and Unicode

Total characters286917
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish
ValueCountFrequency (%)
english 26808
63.0%
français 2424
 
5.7%
italiano 1410
 
3.3%
日本語 1386
 
3.3%
deutsch 1299
 
3.1%
español 1143
 
2.7%
pусский 905
 
2.1%
हिन्दी 546
 
1.3%
한국어/조선말 446
 
1.0%
普通话 413
 
1.0%
Other values (67) 5768
 
13.6%
2024-07-07T16:43:16.243547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 34539
12.0%
n 32163
11.2%
i 31608
11.0%
l 30012
10.5%
h 28164
9.8%
E 27994
9.8%
g 27916
9.7%
a 11070
 
3.9%
o 4017
 
1.4%
r 3564
 
1.2%
Other values (160) 55870
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 34539
12.0%
n 32163
11.2%
i 31608
11.0%
l 30012
10.5%
h 28164
9.8%
E 27994
9.8%
g 27916
9.7%
a 11070
 
3.9%
o 4017
 
1.4%
r 3564
 
1.2%
Other values (160) 55870
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 34539
12.0%
n 32163
11.2%
i 31608
11.0%
l 30012
10.5%
h 28164
9.8%
E 27994
9.8%
g 27916
9.7%
a 11070
 
3.9%
o 4017
 
1.4%
r 3564
 
1.2%
Other values (160) 55870
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 34539
12.0%
n 32163
11.2%
i 31608
11.0%
l 30012
10.5%
h 28164
9.8%
E 27994
9.8%
g 27916
9.7%
a 11070
 
3.9%
o 4017
 
1.4%
r 3564
 
1.2%
Other values (160) 55870
19.5%

spoken_languages_iso
Text

MISSING 

Distinct116
Distinct (%)0.3%
Missing3766
Missing (%)8.3%
Memory size2.1 MiB
2024-07-07T16:43:16.422084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters83160
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 26808
64.5%
fr 2424
 
5.8%
it 1410
 
3.4%
ja 1386
 
3.3%
de 1299
 
3.1%
es 1143
 
2.7%
ru 905
 
2.2%
hi 546
 
1.3%
ko 446
 
1.1%
zh 413
 
1.0%
Other values (106) 4800
 
11.5%
2024-07-07T16:43:16.713803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 29571
35.6%
n 27668
33.3%
r 3950
 
4.7%
f 2872
 
3.5%
i 2369
 
2.8%
a 2227
 
2.7%
t 2178
 
2.6%
s 1914
 
2.3%
d 1607
 
1.9%
j 1388
 
1.7%
Other values (16) 7416
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29571
35.6%
n 27668
33.3%
r 3950
 
4.7%
f 2872
 
3.5%
i 2369
 
2.8%
a 2227
 
2.7%
t 2178
 
2.6%
s 1914
 
2.3%
d 1607
 
1.9%
j 1388
 
1.7%
Other values (16) 7416
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29571
35.6%
n 27668
33.3%
r 3950
 
4.7%
f 2872
 
3.5%
i 2369
 
2.8%
a 2227
 
2.7%
t 2178
 
2.6%
s 1914
 
2.3%
d 1607
 
1.9%
j 1388
 
1.7%
Other values (16) 7416
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29571
35.6%
n 27668
33.3%
r 3950
 
4.7%
f 2872
 
3.5%
i 2369
 
2.8%
a 2227
 
2.7%
t 2178
 
2.6%
s 1914
 
2.3%
d 1607
 
1.9%
j 1388
 
1.7%
Other values (16) 7416
 
8.9%

genres_ids
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)< 0.1%
Missing2384
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean937.60851
Minimum12
Maximum10770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:16.843826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile14
Q118
median28
Q353
95-th percentile10749
Maximum10770
Range10758
Interquartile range (IQR)35

Descriptive statistics

Standard deviation2925.4542
Coefficient of variation (CV)3.1201233
Kurtosis6.936056
Mean937.60851
Median Absolute Deviation (MAD)10
Skewness2.9839567
Sum40281537
Variance8558282.4
MonotonicityNot monotonic
2024-07-07T16:43:16.960973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
18 11948
26.3%
35 8815
19.4%
28 4484
 
9.9%
99 3402
 
7.5%
27 2619
 
5.8%
80 1683
 
3.7%
53 1663
 
3.7%
12 1508
 
3.3%
10749 1191
 
2.6%
16 1122
 
2.5%
Other values (10) 4527
 
10.0%
(Missing) 2384
 
5.3%
ValueCountFrequency (%)
12 1508
 
3.3%
14 703
 
1.6%
16 1122
 
2.5%
18 11948
26.3%
27 2619
 
5.8%
28 4484
 
9.9%
35 8815
19.4%
36 278
 
0.6%
37 451
 
1.0%
53 1663
 
3.7%
ValueCountFrequency (%)
10770 389
 
0.9%
10769 118
 
0.3%
10752 379
 
0.8%
10751 524
 
1.2%
10749 1191
 
2.6%
10402 487
 
1.1%
9648 552
 
1.2%
878 646
 
1.4%
99 3402
7.5%
80 1683
3.7%

genres_names
Categorical

MISSING 

Distinct20
Distinct (%)< 0.1%
Missing2384
Missing (%)5.3%
Memory size2.4 MiB
Drama
11948 
Comedy
8815 
Action
4484 
Documentary
3402 
Horror
2619 
Other values (15)
11694 

Length

Max length15
Median length11
Mean length6.532005
Min length3

Characters and Unicode

Total characters280628
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimation
2nd rowAdventure
3rd rowRomance
4th rowComedy
5th rowComedy

Common Values

ValueCountFrequency (%)
Drama 11948
26.3%
Comedy 8815
19.4%
Action 4484
 
9.9%
Documentary 3402
 
7.5%
Horror 2619
 
5.8%
Crime 1683
 
3.7%
Thriller 1663
 
3.7%
Adventure 1508
 
3.3%
Romance 1191
 
2.6%
Animation 1122
 
2.5%
Other values (10) 4527
 
10.0%
(Missing) 2384
 
5.3%

Length

2024-07-07T16:43:17.090600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama 11948
27.2%
comedy 8815
20.0%
action 4484
 
10.2%
documentary 3402
 
7.7%
horror 2619
 
6.0%
crime 1683
 
3.8%
thriller 1663
 
3.8%
adventure 1508
 
3.4%
romance 1191
 
2.7%
animation 1122
 
2.6%
Other values (12) 5562
12.6%

Most occurring characters

ValueCountFrequency (%)
a 31920
11.4%
r 31502
11.2%
m 28685
10.2%
o 25683
 
9.2%
e 23023
 
8.2%
n 15393
 
5.5%
D 15350
 
5.5%
y 14826
 
5.3%
i 13808
 
4.9%
t 13146
 
4.7%
Other values (19) 67292
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 280628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 31920
11.4%
r 31502
11.2%
m 28685
10.2%
o 25683
 
9.2%
e 23023
 
8.2%
n 15393
 
5.5%
D 15350
 
5.5%
y 14826
 
5.3%
i 13808
 
4.9%
t 13146
 
4.7%
Other values (19) 67292
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 280628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 31920
11.4%
r 31502
11.2%
m 28685
10.2%
o 25683
 
9.2%
e 23023
 
8.2%
n 15393
 
5.5%
D 15350
 
5.5%
y 14826
 
5.3%
i 13808
 
4.9%
t 13146
 
4.7%
Other values (19) 67292
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 280628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 31920
11.4%
r 31502
11.2%
m 28685
10.2%
o 25683
 
9.2%
e 23023
 
8.2%
n 15393
 
5.5%
D 15350
 
5.5%
y 14826
 
5.3%
i 13808
 
4.9%
t 13146
 
4.7%
Other values (19) 67292
24.0%

release_year
Real number (ℝ)

Distinct135
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.8828
Minimum1874
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:17.213901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1874
5-th percentile1941
Q11978
median2001
Q32010
95-th percentile2015
Maximum2020
Range146
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.05304
Coefficient of variation (CV)0.01207553
Kurtosis0.84037057
Mean1991.8828
Median Absolute Deviation (MAD)12
Skewness-1.2247867
Sum90323919
Variance578.54874
MonotonicityNot monotonic
2024-07-07T16:43:17.357423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 1973
 
4.4%
2015 1904
 
4.2%
2013 1887
 
4.2%
2012 1721
 
3.8%
2011 1666
 
3.7%
2016 1604
 
3.5%
2009 1585
 
3.5%
2010 1501
 
3.3%
2008 1470
 
3.2%
2007 1319
 
2.9%
Other values (125) 28716
63.3%
ValueCountFrequency (%)
1874 1
 
< 0.1%
1878 1
 
< 0.1%
1883 1
 
< 0.1%
1887 1
 
< 0.1%
1888 2
 
< 0.1%
1890 5
 
< 0.1%
1891 6
< 0.1%
1892 3
 
< 0.1%
1893 1
 
< 0.1%
1894 13
< 0.1%
ValueCountFrequency (%)
2020 1
 
< 0.1%
2018 5
 
< 0.1%
2017 532
 
1.2%
2016 1604
3.5%
2015 1904
4.2%
2014 1973
4.4%
2013 1887
4.2%
2012 1721
3.8%
2011 1666
3.7%
2010 1501
3.3%

return
Real number (ℝ)

SKEWED  ZEROS 

Distinct5232
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean660.47916
Minimum0
Maximum12396383
Zeros39971
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size354.4 KiB
2024-07-07T16:43:17.503727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.5347418
Maximum12396383
Range12396383
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74717.996
Coefficient of variation (CV)113.12695
Kurtosis20659.288
Mean660.47916
Median Absolute Deviation (MAD)0
Skewness138.28379
Sum29950088
Variance5.582779 × 109
MonotonicityNot monotonic
2024-07-07T16:43:17.648919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39971
88.1%
1 20
 
< 0.1%
2 12
 
< 0.1%
4 11
 
< 0.1%
5 8
 
< 0.1%
3 7
 
< 0.1%
1.333333333 7
 
< 0.1%
2.5 7
 
< 0.1%
1.5 6
 
< 0.1%
4.666666667 4
 
< 0.1%
Other values (5222) 5293
 
11.7%
ValueCountFrequency (%)
0 39971
88.1%
5.217391304 × 10-71
 
< 0.1%
7.5 × 10-71
 
< 0.1%
9.375 × 10-71
 
< 0.1%
1.499133126 × 10-61
 
< 0.1%
1.8 × 10-61
 
< 0.1%
1.916666667 × 10-61
 
< 0.1%
3.5 × 10-61
 
< 0.1%
4 × 10-61
 
< 0.1%
5.111111111 × 10-61
 
< 0.1%
ValueCountFrequency (%)
12396383 1
< 0.1%
8500000 1
< 0.1%
4197476.625 1
< 0.1%
2755584 1
< 0.1%
1018619.283 1
< 0.1%
1000000 1
< 0.1%
26881.72043 1
< 0.1%
12890.38667 1
< 0.1%
5330.33945 1
< 0.1%
4133.333333 1
< 0.1%

actores
Text

MISSING 

Distinct42656
Distinct (%)99.2%
Missing2349
Missing (%)5.2%
Memory size13.6 MiB
2024-07-07T16:43:18.058296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4551
Median length1364
Mean length198.06745
Min length4

Characters and Unicode

Total characters8516306
Distinct characters395
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42490 ?
Unique (%)98.8%

Sample

1st rowTom Hanks, Tim Allen, Don Rickles, Jim Varney, Wallace Shawn, John Ratzenberger, Annie Potts, John Morris, Erik von Detten, Laurie Metcalf, R. Lee Ermey, Sarah Freeman, Penn Jillette
2nd rowRobin Williams, Jonathan Hyde, Kirsten Dunst, Bradley Pierce, Bonnie Hunt, Bebe Neuwirth, David Alan Grier, Patricia Clarkson, Adam Hann-Byrd, Laura Bell Bundy, James Handy, Gillian Barber, Brandon Obray, Cyrus Thiedeke, Gary Joseph Thorup, Leonard Zola, Lloyd Berry, Malcolm Stewart, Annabel Kershaw, Darryl Henriques, Robyn Driscoll, Peter Bryant, Sarah Gilson, Florica Vlad, June Lion, Brenda Lockmuller
3rd rowWalter Matthau, Jack Lemmon, Ann-Margret, Sophia Loren, Daryl Hannah, Burgess Meredith, Kevin Pollak
4th rowWhitney Houston, Angela Bassett, Loretta Devine, Lela Rochon, Gregory Hines, Dennis Haysbert, Michael Beach, Mykelti Williamson, Lamont Johnson, Wesley Snipes
5th rowSteve Martin, Diane Keaton, Martin Short, Kimberly Williams-Paisley, George Newbern, Kieran Culkin, BD Wong, Peter Michael Goetz, Kate McGregor-Stewart, Jane Adams, Eugene Levy, Lori Alan
ValueCountFrequency (%)
john 9804
 
0.8%
michael 7451
 
0.6%
david 6181
 
0.5%
robert 5719
 
0.5%
james 5687
 
0.5%
richard 4443
 
0.4%
paul 4313
 
0.4%
peter 3901
 
0.3%
william 3431
 
0.3%
george 3412
 
0.3%
Other values (112933) 1110138
95.3%
2024-07-07T16:43:18.633742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1121611
 
13.2%
a 704559
 
8.3%
e 664974
 
7.8%
n 523889
 
6.2%
, 519240
 
6.1%
r 497134
 
5.8%
i 483747
 
5.7%
o 423609
 
5.0%
l 366293
 
4.3%
s 255769
 
3.0%
Other values (385) 2955481
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8516306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1121611
 
13.2%
a 704559
 
8.3%
e 664974
 
7.8%
n 523889
 
6.2%
, 519240
 
6.1%
r 497134
 
5.8%
i 483747
 
5.7%
o 423609
 
5.0%
l 366293
 
4.3%
s 255769
 
3.0%
Other values (385) 2955481
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8516306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1121611
 
13.2%
a 704559
 
8.3%
e 664974
 
7.8%
n 523889
 
6.2%
, 519240
 
6.1%
r 497134
 
5.8%
i 483747
 
5.7%
o 423609
 
5.0%
l 366293
 
4.3%
s 255769
 
3.0%
Other values (385) 2955481
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8516306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1121611
 
13.2%
a 704559
 
8.3%
e 664974
 
7.8%
n 523889
 
6.2%
, 519240
 
6.1%
r 497134
 
5.8%
i 483747
 
5.7%
o 423609
 
5.0%
l 366293
 
4.3%
s 255769
 
3.0%
Other values (385) 2955481
34.7%

director
Text

MISSING 

Distinct41708
Distinct (%)93.5%
Missing760
Missing (%)1.7%
Memory size5.7 MiB
2024-07-07T16:43:18.924659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2303
Median length853
Mean length74.212757
Min length3

Characters and Unicode

Total characters3308850
Distinct characters204
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39752 ?
Unique (%)89.2%

Sample

1st rowJohn Lasseter, Joss Whedon, Andrew Stanton, nan, nan, nan, nan, nan, nan, Lee Unkrich, Ralph Eggleston, Robert Gordon, nan, nan, nan, nan, nan, nan, John Lasseter, Pete Docter, Joe Ranft, nan, nan, Ash Brannon, nan, nan, nan, nan, nan, nan, nan, nan, nan, Andrew Stanton, Pete Docter, Gary Rydstrom, nan, nan, nan, nan, Colin Brady, nan, nan, nan, nan, Jimmy Hayward, nan, nan, nan, nan, Bud Luckey, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, Doug Sweetland, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, Bud Luckey, Andrew Stanton, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, Gary Rydstrom, nan, nan, Pat Jackson, nan, nan, nan, nan, nan, nan, nan
2nd rownan, Jonathan Hensleigh, nan, Joe Johnston, Robert Dalva, nan, Kyle Balda, nan, nan, nan, nan, nan, nan, nan, nan, nan
3rd rowHoward Deutch, Mark Steven Johnson, Mark Steven Johnson, nan
4th rowForest Whitaker, nan, nan, nan, nan, nan, nan, nan, nan, nan
5th rownan, nan, Nancy Meyers, Nancy Meyers, nan, Charles Shyer, nan
ValueCountFrequency (%)
nan 360241
62.3%
john 2856
 
0.5%
david 2154
 
0.4%
michael 2083
 
0.4%
robert 1850
 
0.3%
james 1312
 
0.2%
peter 1180
 
0.2%
richard 1097
 
0.2%
paul 1025
 
0.2%
william 938
 
0.2%
Other values (18707) 203124
35.2%
2024-07-07T16:43:19.371779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 814729
24.6%
533337
16.1%
a 479779
14.5%
, 419170
12.7%
e 121779
 
3.7%
r 94654
 
2.9%
i 89812
 
2.7%
o 81752
 
2.5%
l 63246
 
1.9%
s 49405
 
1.5%
Other values (194) 561187
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3308850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 814729
24.6%
533337
16.1%
a 479779
14.5%
, 419170
12.7%
e 121779
 
3.7%
r 94654
 
2.9%
i 89812
 
2.7%
o 81752
 
2.5%
l 63246
 
1.9%
s 49405
 
1.5%
Other values (194) 561187
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3308850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 814729
24.6%
533337
16.1%
a 479779
14.5%
, 419170
12.7%
e 121779
 
3.7%
r 94654
 
2.9%
i 89812
 
2.7%
o 81752
 
2.5%
l 63246
 
1.9%
s 49405
 
1.5%
Other values (194) 561187
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3308850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 814729
24.6%
533337
16.1%
a 479779
14.5%
, 419170
12.7%
e 121779
 
3.7%
r 94654
 
2.9%
i 89812
 
2.7%
o 81752
 
2.5%
l 63246
 
1.9%
s 49405
 
1.5%
Other values (194) 561187
17.0%

Interactions

2024-07-07T16:43:03.815993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:48.903753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.306252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.535255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.743315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.009926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.347828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.670398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:58.047668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.568532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.879374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:02.190028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:03.948920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.040310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.410281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.636252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.852402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.121925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.491262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.779393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:58.342115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.679532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.991435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:02.302191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.074104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.154306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.495252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.725251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.946918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.316928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.596289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.891394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:58.433119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.783553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.092968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:02.410758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.176617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.263307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.599253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.820252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.045919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.429010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.704437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.997393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:58.538125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.894622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.194484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:02.518897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.301684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.384306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.690281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.916253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.139954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.533827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.805438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:57.111970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:58.639690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.993335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.294579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:02.631897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.444795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.493307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.782281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.014253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.238925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.628829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.904438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:57.229893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:58.753314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.104560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.398092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:02.769031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.537441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.608306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.871252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.107369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.356954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.723858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.004631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:57.360187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:58.855016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.198072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.530065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:02.884114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.639968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.729306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.970252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.212853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.478928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.825829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.107466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:57.510818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:58.975912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.311069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.639622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:03.161115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.735311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.853308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.066281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.313405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.582953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:54.924829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.207441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:57.619867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.082914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.441471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.737087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:03.262448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.836373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:49.977309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.161254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.419812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.690925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.023828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.322436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:57.728866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.217440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.546582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.875161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:03.389628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:04.933105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.081310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.255254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.518814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.794924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.130830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.426469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:57.824862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.320437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.647182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:01.972247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:03.521762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:05.037650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:50.196244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:51.438282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:52.626746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:53.896926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:55.236827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:56.545836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:57.933893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:42:59.437437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:00.757326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:02.088437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-07T16:43:03.665365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-07-07T16:43:05.234085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-07T16:43:05.684627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-07T16:43:06.214799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

budgetidoriginal_languageoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevote_averagevote_countgenresidgenresnameproduction_companiesnameproduction_companiesidproduction_countriesiso_3166_1production_countriesnamespoken_languagesiso_639_1spoken_languagesnamespoken_languages_namesspoken_languages_isogenres_idsgenres_namesrelease_yearreturnactoresdirector
030000000862enLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.21.9469431995-10-30373554033.081.0ReleasedNaNToy Story7.75415.016.0AnimationPixar Animation Studios3.0USUnited States of AmericaenEnglishEnglishen16.0Animation1995.012.451801Tom Hanks, Tim Allen, Don Rickles, Jim Varney, Wallace Shawn, John Ratzenberger, Annie Potts, John Morris, Erik von Detten, Laurie Metcalf, R. Lee Ermey, Sarah Freeman, Penn JilletteJohn Lasseter, Joss Whedon, Andrew Stanton, nan, nan, nan, nan, nan, nan, Lee Unkrich, Ralph Eggleston, Robert Gordon, nan, nan, nan, nan, nan, nan, John Lasseter, Pete Docter, Joe Ranft, nan, nan, Ash Brannon, nan, nan, nan, nan, nan, nan, nan, nan, nan, Andrew Stanton, Pete Docter, Gary Rydstrom, nan, nan, nan, nan, Colin Brady, nan, nan, nan, nan, Jimmy Hayward, nan, nan, nan, nan, Bud Luckey, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, Doug Sweetland, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, Bud Luckey, Andrew Stanton, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, Gary Rydstrom, nan, nan, Pat Jackson, nan, nan, nan, nan, nan, nan, nan
1650000008844enWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.17.0155391995-12-15262797249.0104.0ReleasedRoll the dice and unleash the excitement!Jumanji6.92413.012.0AdventureTriStar Pictures559.0USUnited States of AmericaenEnglishEnglishen12.0Adventure1995.04.043035Robin Williams, Jonathan Hyde, Kirsten Dunst, Bradley Pierce, Bonnie Hunt, Bebe Neuwirth, David Alan Grier, Patricia Clarkson, Adam Hann-Byrd, Laura Bell Bundy, James Handy, Gillian Barber, Brandon Obray, Cyrus Thiedeke, Gary Joseph Thorup, Leonard Zola, Lloyd Berry, Malcolm Stewart, Annabel Kershaw, Darryl Henriques, Robyn Driscoll, Peter Bryant, Sarah Gilson, Florica Vlad, June Lion, Brenda Lockmullernan, Jonathan Hensleigh, nan, Joe Johnston, Robert Dalva, nan, Kyle Balda, nan, nan, nan, nan, nan, nan, nan, nan, nan
2015602enA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.11.7129001995-12-220.0101.0ReleasedStill Yelling. Still Fighting. Still Ready for Love.Grumpier Old Men6.592.010749.0RomanceWarner Bros.6194.0USUnited States of AmericaenEnglishEnglishen10749.0Romance1995.00.000000Walter Matthau, Jack Lemmon, Ann-Margret, Sophia Loren, Daryl Hannah, Burgess Meredith, Kevin PollakHoward Deutch, Mark Steven Johnson, Mark Steven Johnson, nan
31600000031357enCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.3.8594951995-12-2281452156.0127.0ReleasedFriends are the people who let you be yourself... and never let you forget it.Waiting to Exhale6.134.035.0ComedyTwentieth Century Fox Film Corporation306.0USUnited States of AmericaenEnglishEnglishen35.0Comedy1995.05.090760Whitney Houston, Angela Bassett, Loretta Devine, Lela Rochon, Gregory Hines, Dennis Haysbert, Michael Beach, Mykelti Williamson, Lamont Johnson, Wesley SnipesForest Whitaker, nan, nan, nan, nan, nan, nan, nan, nan, nan
4011862enJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.8.3875191995-02-1076578911.0106.0ReleasedJust When His World Is Back To Normal... He's In For The Surprise Of His Life!Father of the Bride Part II5.7173.035.0ComedySandollar Productions5842.0USUnited States of AmericaenEnglishEnglishen35.0Comedy1995.00.000000Steve Martin, Diane Keaton, Martin Short, Kimberly Williams-Paisley, George Newbern, Kieran Culkin, BD Wong, Peter Michael Goetz, Kate McGregor-Stewart, Jane Adams, Eugene Levy, Lori Alannan, nan, Nancy Meyers, Nancy Meyers, nan, Charles Shyer, nan
560000000949enObsessive master thief, Neil McCauley leads a top-notch crew on various insane heists throughout Los Angeles while a mentally unstable detective, Vincent Hanna pursues him without rest. Each man recognizes and respects the ability and the dedication of the other even though they are aware their cat-and-mouse game may end in violence.17.9249271995-12-15187436818.0170.0ReleasedA Los Angeles Crime SagaHeat7.71886.028.0ActionRegency Enterprises508.0USUnited States of AmericaenEnglishEnglishen28.0Action1995.03.123947Al Pacino, Robert De Niro, Val Kilmer, Jon Voight, Tom Sizemore, Diane Venora, Amy Brenneman, Ashley Judd, Mykelti Williamson, Natalie Portman, Ted Levine, Tom Noonan, Tone Loc, Hank Azaria, Wes Studi, Dennis Haysbert, Danny Trejo, Henry Rollins, William Fichtner, Kevin Gage, Susan Traylor, Jerry Trimble, Ricky Harris, Jeremy Piven, Xander Berkeley, Begonya Plaza, Rick Avery, Hazelle Goodman, Ray Buktenica, Max Daniels, Vince Deadrick Jr., Steven Ford, Farrah Forke, Patricia Healy, Paul Herman, Cindy Katz, Brian Libby, Dan Martin, Mario Roberts, Thomas Rosales, Jr., Yvonne Zima, Mick Gould, Bud Cort, Viviane Vives, Kim Staunton, Martin Ferrero, Brad Baldridge, Andrew Camuccio, Kenny Endoso, Kimberly Flynn, Niki Harris, Bill McIntosh, Rick Marzan, Terry Miller, Daniel O'Haco, Kai Soremekun, Peter Blackwell, Trevor Coppola, Mary Kircher, Darin Mangan, Robert Miranda, Manny Perry, Iva Franks Singer, Tim Werner, Philip EttingtonMichael Mann, Michael Mann, Art Linson, Michael Mann, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, James Muro, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, Pieter Jan Brugge, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan
65800000011860enAn ugly duckling having undergone a remarkable change, still harbors feelings for her crush: a carefree playboy, but not before his business-focused brother has something to say about it.6.6772771995-12-150.0127.0ReleasedYou are cordially invited to the most surprising merger of the year.Sabrina6.2141.035.0ComedyParamount Pictures4.0DEGermanyfrFrançaisFrançaisfr35.0Comedy1995.00.000000Harrison Ford, Julia Ormond, Greg Kinnear, Angie Dickinson, Nancy Marchand, John Wood, Richard Crenna, Lauren Holly, Dana Ivey, Fanny Ardant, Patrick Bruel, Paul Giamatti, Miriam Colón, Elizabeth Franz, Valérie Lemercier, Becky Ann Baker, John C. Vennema, Margo Martindale, J. Smith-Cameron, Christine Luneau-Lipton, Michael Dees, Denis Holmes, Jo-Jo Lowe, Ira Wheeler, Philippa Cooper, Ayako Kawahara, François Genty, Guillaume Gallienne, Inés Sastre, Phina Oruche, Andrea Behalikova, Jennifer Herrera, Kristina Kumlin, Eva Linderholm, Carmen Chaplin, Micheline Van de Velde, Joanna Rhodes, Alan Boone, Patrick Forster-Delmas, Kentaro Matsuo, Peter McKernan, Ed Connelly, Ronald L. Schwary, Alvin Lum, Siching Song, Phil Nee, Randy Becker, Susan Browning, Anthony Mondal, Peter Parks, Woodrow Asai, Eric Bruno Borgman, Michael Cline, Christopher Del Gaudio, Philippe Hartmann, Jerry Quinn, Dori RosenthalSydney Pollack, nan, Sydney Pollack, John Williams, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, Gary Jones, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan
7045325enA mischievous young boy, Tom Sawyer, witnesses a murder by the deadly Injun Joe. Tom becomes friends with Huckleberry Finn, a boy with no future and no family. Tom has to choose between honoring a friendship or honoring an oath because the town alcoholic is accused of the murder. Tom and Huck go through several adventures trying to retrieve evidence.2.5611611995-12-220.097.0ReleasedThe Original Bad Boys.Tom and Huck5.445.028.0ActionWalt Disney Pictures2.0USUnited States of AmericaenEnglishEnglishen28.0Action1995.00.000000Jonathan Taylor Thomas, Brad Renfro, Rachael Leigh Cook, Michael McShane, Amy Wright, Eric Schweig, Tamara Mellonan, Stephen Sommers, Peter Hewitt, nan
8350000009091enInternational action superstar Jean Claude Van Damme teams with Powers Boothe in a Tension-packed, suspense thriller, set against the back-drop of a Stanley Cup game.Van Damme portrays a father whose daughter is suddenly taken during a championship hockey game. With the captors demanding a billion dollars by game's end, Van Damme frantically sets a plan in motion to rescue his daughter and abort an impending explosion before the final buzzer...5.2315801995-12-2264350171.0106.0ReleasedTerror goes into overtime.Sudden Death5.5174.028.0ActionUniversal Pictures33.0USUnited States of AmericaenEnglishEnglishen28.0Action1995.01.838576Jean-Claude Van Damme, Powers Boothe, Dorian Harewood, Raymond J. Barry, Ross Malinger, Whittni WrightPeter Hyams, nan, Gene Quintano, nan, nan, nan, nan, Peter Hyams, nan
958000000710enJames Bond must unmask the mysterious head of the Janus Syndicate and prevent the leader from utilizing the GoldenEye weapons system to inflict devastating revenge on Britain.14.6860361995-11-16352194034.0130.0ReleasedNo limits. No fears. No substitutes.GoldenEye6.61194.012.0AdventureUnited Artists60.0GBUnited KingdomenEnglishEnglishen12.0Adventure1995.06.072311Pierce Brosnan, Sean Bean, Izabella Scorupco, Famke Janssen, Joe Don Baker, Judi Dench, Gottfried John, Robbie Coltrane, Alan Cumming, Tchéky Karyo, Desmond Llewelyn, Samantha Bond, Michael Kitchen, Serena Gordon, Simon Kunz, Billy J. Mitchell, Constantine Gregory, Minnie Driver, Michelle Arthur, Ravil IsyanovMartin Campbell, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan
budgetidoriginal_languageoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevote_averagevote_countgenresidgenresnameproduction_companiesnameproduction_companiesidproduction_countriesiso_3166_1production_countriesnamespoken_languagesiso_639_1spoken_languagesnamespoken_languages_namesspoken_languages_isogenres_idsgenres_namesrelease_yearreturnactoresdirector
45336067179itSentenced to life imprisonment for illegal activities, Italian International member Giulio Manieri holds on to his political ideals while struggling against madness in the loneliness of his prison cell.0.2250511972-01-010.090.0ReleasedNaNSt. Michael Had a Rooster6.03.0NaNNaNNaNNaNNaNNaNitItalianoItalianoitNaNNaN1972.00.0Giulio Brogi, Renato Cestiè, Vito Cipolla, Daniele Dublinonan, Paolo Taviani, Paolo Taviani, Vittorio Taviani
45337084419enAn unsuccessful sculptor saves a madman named "The Creeper" from drowning. Seeing an opportunity for revenge, he tricks the psycho into murdering his critics.0.2228141946-03-290.065.0ReleasedMeet...The CREEPER!House of Horrors6.38.027.0HorrorUniversal Pictures33.0USUnited States of AmericaenEnglishEnglishen27.0Horror1946.00.0Rondo Hatton, Robert Lowery, Virginia Grey, Bill Goodwin, Martin Kosleck, Alan Napier, Howard Freeman, Virginia Christine, Joan Shawlee, Byron Foulger, Syd Saylornan, nan, nan, nan, Jean Yarbrough, nan, nan, nan, nan, nan, nan
453380390959enIn this true-crime documentary, we delve into the murder spree that was the inspiration for Joe Berlinger's "Book of Shadows: Blair Witch 2".0.0760612000-10-220.045.0ReleasedNaNShadow of the Blair Witch7.02.09648.0MysteryNaNNaNNaNNaNenEnglishEnglishen9648.0Mystery2000.00.0Tony Abatemarco, Andre Brooks, Mariclare Costello, Bill Dreggors, Apollo Dukakis, Philip Friedman, James Gleason, Dilva Henry, Bari Hochwald, Wendy Hoffman, John Huck, Rachel Moskowitz, Sandy Mulvihill, Roger Nolan, Chris Parnell, Byrne Piven, Richard Sexton, Rich Williams, Ray XifoBen Rock, Ben Rock, nan, nan, nan, Ben Rock, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan
453390289923enA film archivist revisits the story of Rustin Parr, a hermit thought to have murdered seven children while under the possession of the Blair Witch.0.3864502000-10-030.030.0ReleasedDo you know what happened 50 years before "The Blair Witch Project"?The Burkittsville 77.01.027.0HorrorNeptune Salad Entertainment27570.0USUnited States of AmericaenEnglishEnglishen27.0Horror2000.00.0Monty Bane, Lucy Butler, David Grammer, Bill Dreggors, Frank Pastor, Heather Donahue, Joshua Leonard, Michael C. WilliamsBen Rock, Ben Rock
453400222848enIt's the year 3000 AD. The world's most dangerous women are banished to a remote asteroid 45 million light years from earth. Kira Murphy doesn't belong; wrongfully accused of a crime she did not commit, she's thrown in this interplanetary prison and left to her own defenses. But Kira's a fighter, and soon she finds herself in the middle of a female gang war; where everyone wants a piece of the action... and a piece of her! "Caged Heat 3000" takes the Women-in-Prison genre to a whole new level... and a whole new galaxy!0.6615581995-01-010.085.0ReleasedNaNCaged Heat 30003.51.0878.0Science FictionConcorde-New Horizons4688.0USUnited States of AmericaenEnglishEnglishen878.0Science Fiction1995.00.0Lisa Boyle, Kena Land, Zaneta Polard, Don Yanan, Debra K. Beatty, Mark Sikes, Robert J. Ferrelli, Ellyn Dawn Humphreys, Ron Jeremy, Ben RamseyRoger Corman, Mike Elliott, Aaron Osborne, nan, nan, nan
45341030840enYet another version of the classic epic, with enough variation to make it interesting. The story is the same, but some of the characters are quite different from the usual, in particular Uma Thurman's very special maid Marian. The photography is also great, giving the story a somewhat darker tone.5.6837531991-05-130.0104.0ReleasedNaNRobin Hood5.726.018.0DramaWestdeutscher Rundfunk (WDR)7025.0CACanadaenEnglishEnglishen18.0Drama1991.00.0Patrick Bergin, Uma Thurman, David Morrissey, Jürgen Prochnow, Jeroen KrabbéJohn Irvin, nan, nan, nan, nan, nan, nan, nan, nan
453420111109tlAn artist struggles to finish his work while a storyline about a cult plays in his head.0.1782412011-11-170.0360.0ReleasedNaNCentury of Birthing9.03.018.0DramaSine Olivia19653.0PHPhilippinestlNaNNaNtl18.0Drama2011.00.0Angel Aquino, Perry Dizon, Hazel Orencio, Joel Torre, Bart Guingona, Soliman Cruz , Roeder, Angeli Bayani, Dante Perez, Betty Uy-Regala, ModestaLav Diaz, Lav Diaz, nan, Lav Diaz, Lav Diaz, Lav Diaz
45343067758enWhen one of her hits goes wrong, a professional assassin ends up with a suitcase full of a million dollars belonging to a mob boss ...0.9030072003-08-010.090.0ReleasedA deadly game of wits.Betrayal3.86.028.0ActionAmerican World Pictures6165.0USUnited States of AmericaenEnglishEnglishen28.0Action2003.00.0Erika Eleniak, Adam Baldwin, Julie du Page, James Remar, Damian Chapa, Louis Mandylor, Tom Wright, Jeremy Lelliott, James Quattrochi, Jason Widener, Joe Sabatino, Kiko Ellsworth, Don Swayze, Peter Dobson, Darrell DubovskyMark L. Lester, C. Courtney Joyner, nan, nan, nan
453440227506enIn a small town live two brothers, one a minister and the other one a hunchback painter of the chapel who lives with his wife. One dreadful and stormy night, a stranger knocks at the door asking for shelter. The stranger talks about all the good things of the earthly life the minister is missing because of his puritanical faith. The minister comes to accept the stranger's viewpoint but it is others who will pay the consequences because the minister will discover the human pleasures thanks to, ehem, his sister- in -law… The tormented minister and his cuckolded brother will die in a strange accident in the chapel and later an infant will be born from the minister's adulterous relationship.0.0035031917-10-210.087.0ReleasedNaNSatan Triumphant0.00.0NaNNaNYermoliev88753.0RURussiaNaNNaNNaNNaNNaNNaN1917.00.0Iwan Mosschuchin, Nathalie Lissenko, Pavel Pavlov, Aleksandr Chabrov, Vera OrlovaYakov Protazanov, nan
453450461257en50 years after decriminalisation of homosexuality in the UK, director Daisy Asquith mines the jewels of the BFI archive to take us into the relationships, desires, fears and expressions of gay men and women in the 20th century.0.1630152017-06-090.075.0ReleasedNaNQueerama0.00.0NaNNaNNaNNaNGBUnited KingdomenEnglishEnglishenNaNNaN2017.00.0NaNDaisy Asquith